Fine-Tuning the Pytorch I3D Model on a Custom Dataset скачать в хорошем качестве

Fine-Tuning the Pytorch I3D Model on a Custom Dataset

Learn how to fine-tune the Pytorch I3D model for your custom dataset with practical coding examples and explanations. Boost your video processing model's performance today! --- This video is based on the question https://stackoverflow.com/q/77645015/ asked by the user 'Carlo' ( https://stackoverflow.com/u/4537160/ ) and on the answer https://stackoverflow.com/a/77645449/ provided by the user 'rot8' ( https://stackoverflow.com/u/10586234/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions. Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: Fine-tune Pytorch I3D model on a custom dataset Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l... The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license. If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com. --- Fine-Tuning the Pytorch I3D Model on a Custom Dataset: A Step-by-Step Guide Introduction If you are looking to optimize the performance of your video processing tasks using deep learning, fine-tuning a pre-trained model can be a highly effective approach. This guide delves into the specifics of fine-tuning the I3D (Inflated 3D) model from Pytorch hub on a custom dataset—specifically, one with four output classes. The Problem You might have stumbled upon the need to adjust a pre-trained model to better suit your unique dataset. You've loaded the I3D model, which was trained on the Kinetics 400 dataset, but now you're facing a challenge: modifying the last layer of the model to output predictions for your four classes instead of the original 400. Here's the error that you encountered when trying to adapt the model: [[See Video to Reveal this Text or Code Snippet]] In this guide, we will explore how to effectively resolve this issue and achieve your desired output. Solution Overview The goal is to modify the projection layer of the ResNetBasicHead to output the exact number of classes you want. We'll break down the steps needed to achieve this, ensuring you can follow along effortlessly. 1. Load the Pre-Trained I3D Model First, we need to import the required libraries and load the I3D model. You can accomplish this using Pytorch hub: [[See Video to Reveal this Text or Code Snippet]] 2. Modify the Output Layer To change the output layer of the model, you basically want to add a new linear layer that reflects the number of classes present in your custom dataset (in this case, 4 classes). The key steps include: Understanding the structure of the ResNetBasicHead. Correctly appending a new linear layer to it. 3. Adding the New Linear Layer Here’s how you can append a new linear layer to the network: Option 1: Append to the Model's Blocks You can add the linear layer directly to the model’s block structure: [[See Video to Reveal this Text or Code Snippet]] After this, your model structure will look like this: [[See Video to Reveal this Text or Code Snippet]] Option 2: Modify Existing ResNetBasicHead If you prefer to add the new layer directly to the existing ResNetBasicHead, you can do so like this: [[See Video to Reveal this Text or Code Snippet]] This will lead to the following modified structure: [[See Video to Reveal this Text or Code Snippet]] Conclusion Fine-tuning the I3D model to suit your custom dataset doesn't have to be a daunting task. By understanding the model's structure and modifying it correctly, you can adapt the I3D model to produce outputs reflective of your specific needs. You will now be better equipped to leverage the power of pre-trained models for your video classification tasks! Whether you chose to append the new layer to the blocks or alter the existing ResNetBasicHead, the key takeaway is that with a little tweak, your model can be customized to yield excellent performance on your own data. Happy coding!